1. Field of the Invention
The present invention generally relates to a system and method for evaluating and optimizing promotional sale of products based on historical data.
2. Description of the Prior Art
Evaluators and optimizers are two types of systems for studying promotional plans for products. Evaluators evaluate a promotional plan to reveal whether the implementation of that plan would cause the sales desired by the user. Optimizers use evaluators to develop new promotion plans or to suggest changes to existing promotion plans.
Presently, there exist systems and models to evaluate and/or optimize promotions for products. These systems include the Promotion Simulator available by A. C. Nielsen, the model developed by A. L. Montgomery at the Wharton School of Business in the paper Creating Micro-Marketing Pricing Strategies Using Supermarket Scanner Data (1995), and the model developed by D. F. Midgley, R. E. Marks, and L. G. Cooper at the Santa Fe Institute in the paper Breeding Competitive Strategies.
The Promotion Simulator is only an evaluator of promotion plans implemented using linear regression. This system takes one product and one promotion and evaluates the promotion. For example, for the product `X` brand shampoo and `Y` promotion, this system would answer the question "would promotion `Y` increase profits of `X` brand shampoo by x %".
The model developed at the Wharton School of Business is not a physically implemented system but only an algorithm. This algorithm employs logarithmic regression to implement an evaluator and an optimizer, and determines what promotion for product "Y" would increase profits by x %. However, the data evaluated and optimized by this system only includes price in estimating the effects of competition.
The model developed at the Santa Fe Institute is a theoretical algorithm for an optimizer. This algorithm takes as input a pay-off function describing the expected benefits for a promotion. This function may be determined empirically, or may be set experimentally. The algorithm, a genetic algorithm, then creates an optimal promotion plan in the face of competition which meets budget constraints.
While the prior art techniques described above are useful to a limited degree, none combine both an evaluator and an optimizer in a physical system to develop new promotional plans or to analyze existing plans for various products using sophisticated historical data of related products and competitors.